Elizabeth Healey

Elizabeth Healey

Ph.D. Candidate working on Machine Learning for Healthcare

Hello! I am a PhD candidate in the Harvard–MIT Health Sciences and Technology program advised by Isaac Kohane and supported by the National Science Foundation GRFP. My research focus is in harnessing physiological signals and observational health data to enhance clinical decision-making and understand disease heterogeneity. My ongoing work concentrates on using continuous glucose monitoring to enable precision medicine for patients with type 2 diabetes.

Previously, I graduated magna cum laude from Harvard College with an S.B. degree in Electrical Engineering where I worked on biomedical control for the artificial pancreas.

  • Machine learning for precision medicine
  • Clinical decision support
  • Wearable technology
  • Diabetes Management
  • Massachusetts Institute of Technology

    PhD in Medical Engineering and Medical Physics

  • Harvard University

    BSc in Electrical Engineering, 2019


Selected publications are below

Elizabeth Healey, Isaac Kohane. Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring. Accepted at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2024. (Oral Presentation).

Elizabeth Healey, Kristen Flint, Jessica Ruiz, Amelia Tan. Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study. 2024. medRxiv doi: 10.1101/2024.04.06.24305022

Kun-Hsing Yu, Elizabeth Healey, Tze-Yun Leong, Isaac Kohane, Arjun Manrai. Medical Artificial Intelligence and Human Values. New England Journal of Medicine. 2024.

Peniel Argaw*, Elizabeth Healey*, Isaac Kohane. Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals. Machine Learning for Health Symposium. 2022.

Ankush Chakrabarty, Elizabeth Healey, Dawei Shi, Stamatina Zavitsanou, Francis J Doyle and Eyal Dassau. Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE Transactions on Control Systems Technology. 2019.


I have been on the teaching team of multiple graduate and undergraduate courses during my training across topics in electrical engineering, machine learning, and translational methods in bioinformatics. I was previously a Teaching Development Fellow and earned a teaching certificate by completing the Graduate Teaching Development tracks at MIT.

AISC 610: Computationally-Enabled Medicine
BMI 703: Precision Medicine I - Genomic Medicine
BMI 707: Deep Learning for Biomedical Data
ES 152: Circuits, Devices, and Transduction
ES52: The Joy of Electronics